Engineered Autonomous Human Variable Domains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The complex multi-chain architecture of antibodies has spurred interest in smaller derivatives that retain specificity but can be more easily produced in bacteria. Domain antibodies consisting of single variable domains are the smallest antibody fragments and have been shown to possess enhanced ability to target epitopes that are difficult to access using multidomain antibodies. However, in contrast to natural camelid antibody domains, human variable domains typically suffer from low stability and high propensity to aggregate. METHODS: This review summarizes strategies to improve the biophysical properties of heavy chain variable domains from human antibodies with an emphasis on aggregation resistance. Several protein engineering approaches have targeted antibody frameworks and complementarity determining regions to stabilize the native state and prevent aggregation of the denatured state. CONCLUSION: Recent findings enable the construction of highly diverse libraries enriched in aggregation-resistant variants that are expected to provide binders to diverse antigens. Engineered domain antibodies possess unique advantages in expression, epitope preference and flexibility of formatting over conventional immunoreagents and are a promising class of antibody fragments for biomedical development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it